论文标题
DEEPCSI：通过MU-MIMO CSI反馈深度学习重新思考Wi-Fi无线电指纹
DeepCSI: Rethinking Wi-Fi Radio Fingerprinting Through MU-MIMO CSI Feedback Deep Learning
论文作者
论文摘要
我们提出了DeepCSI，这是一种新型的Wi-Fi无线电指纹（RFP），该方法利用标准符合标准的波束形成反馈矩阵在移动中对Mu-Mimo Wi-Fi设备进行身份验证。通过在现成的无线电电路中捕获独特的缺陷，RFP技术可以直接在物理层处识别无线设备，从而允许低延迟的低能量加密性身份验证。但是，现有的Wi-Fi RFP技术基于软件定义的无线电（SDR），这最终可能阻止其广泛采用。此外，目前尚不清楚现有策略是否可以在MU-MIMO发射机的存在下（现代Wi-Fi标准中的一项关键技术）有效。相反，从先前的工作中，DEEPCSI不需要SDR技术，并且可以在任何低成本Wi-Fi设备上运行以验证MU-MIMO发射机。我们的关键直觉是，发射机无线电电路中的缺陷将渗透到波束成形的反馈矩阵上，因此可以在没有明确的通道状态信息（CSI）计算的情况下执行RFP。 DeepCSI对流过的际交往和用户间干扰是强大的，是不受这些现象影响的光束形成反馈。我们通过现成的设备在野外进行的大规模数据收集活动来广泛评估DEEPCSI的性能，那里有10个Mu-Mimo Wi-Fi无线电在不同位置发出的信号。实验结果表明，DEEPCSI正确识别发射机的精度高达98％。当设备在环境中移动时，识别精度仍高于82％。为了允许可复制性并提供性能基准，我们保证共享800 GB数据集 - 首次在静态和动态条件下收集 - 以及与社区的代码数据库。
We present DeepCSI, a novel approach to Wi-Fi radio fingerprinting (RFP) which leverages standard-compliant beamforming feedback matrices to authenticate MU-MIMO Wi-Fi devices on the move. By capturing unique imperfections in off-the-shelf radio circuitry, RFP techniques can identify wireless devices directly at the physical layer, allowing low-latency low-energy cryptography-free authentication. However, existing Wi-Fi RFP techniques are based on software-defined radio (SDRs), which may ultimately prevent their widespread adoption. Moreover, it is unclear whether existing strategies can work in the presence of MU-MIMO transmitters - a key technology in modern Wi-Fi standards. Conversely from prior work, DeepCSI does not require SDR technologies and can be run on any low-cost Wi-Fi device to authenticate MU-MIMO transmitters. Our key intuition is that imperfections in the transmitter's radio circuitry percolate onto the beamforming feedback matrix, and thus RFP can be performed without explicit channel state information (CSI) computation. DeepCSI is robust to inter-stream and inter-user interference being the beamforming feedback not affected by those phenomena. We extensively evaluate the performance of DeepCSI through a massive data collection campaign performed in the wild with off-the-shelf equipment, where 10 MU-MIMO Wi-Fi radios emit signals in different positions. Experimental results indicate that DeepCSI correctly identifies the transmitter with an accuracy of up to 98%. The identification accuracy remains above 82% when the device moves within the environment. To allow replicability and provide a performance benchmark, we pledge to share the 800 GB datasets - collected in static and, for the first time, dynamic conditions - and the code database with the community.
